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Article

Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver

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Department of Diagnostic Medical Imaging, Madou Sin-Lau Hospital, Tainan 721, Taiwan
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Department of Finance, Chung Yuan Christian University, Chung Li 320, Taiwan
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Department of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
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Development and Alumni Relations, University of Cambridge, Cambridge CB5 8AB, UK
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Department of Mathematics, National Central University, Taoyuan City 320, Taiwan
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Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong
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Global Health Program, College of Public Health, National Taiwan University, Taipei City 100, Taiwan
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Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City 100, Taiwan
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Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London SE5 8AF, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Emilio Quaia
Tomography 2021, 7(4), 555-572; https://doi.org/10.3390/tomography7040048
Received: 3 August 2021 / Revised: 26 September 2021 / Accepted: 27 September 2021 / Published: 8 October 2021
(This article belongs to the Section Brain Imaging)
In order to improve the image quality of BLADE magnetic resonance imaging (MRI) using the index tensor solvers and to evaluate MRI image quality in a clinical setting, we implemented BLADE MRI reconstructions using two tensor solvers (the least-squares solver and the L1 total-variation regularized least absolute deviation (L1TV-LAD) solver) on a graphics processing unit (GPU). The BLADE raw data were prospectively acquired and presented in random order before being assessed by two independent radiologists. Evaluation scores were examined for consistency and then by repeated measures analysis of variance (ANOVA) to identify the superior algorithm. The simulation showed the structural similarity index (SSIM) of various tensor solvers ranged between 0.995 and 0.999. Inter-reader reliability was high (Intraclass correlation coefficient (ICC) = 0.845, 95% confidence interval: 0.817, 0.87). The image score of L1TV-LAD was significantly higher than that of vendor-provided image and the least-squares method. The image score of the least-squares method was significantly lower than that of the vendor-provided image. No significance was identified in L1TV-LAD with a regularization strength of λ= 0.4–1.0. The L1TV-LAD with a regularization strength of λ= 0.4–0.7 was found consistently better than least-squares and vendor-provided reconstruction in BLADE MRI with a SENSitivity Encoding (SENSE) factor of 2. This warrants further development of the integrated computing system with the scanner. View Full-Text
Keywords: BLADE MRI; least absolute deviation; graphic processing unit (GPU); non-uniform fast Fourier transform (NUFFT) BLADE MRI; least absolute deviation; graphic processing unit (GPU); non-uniform fast Fourier transform (NUFFT)
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MDPI and ACS Style

Chen, H.-C.; Yang, H.-C.; Chen, C.-C.; Harrevelt, S.; Chao, Y.-C.; Lin, J.-M.; Yu, W.-H.; Chang, H.-C.; Chang, C.-K.; Hwang, F.-N. Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver. Tomography 2021, 7, 555-572. https://doi.org/10.3390/tomography7040048

AMA Style

Chen H-C, Yang H-C, Chen C-C, Harrevelt S, Chao Y-C, Lin J-M, Yu W-H, Chang H-C, Chang C-K, Hwang F-N. Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver. Tomography. 2021; 7(4):555-572. https://doi.org/10.3390/tomography7040048

Chicago/Turabian Style

Chen, Hsin-Chia, Haw-Chiao Yang, Chih-Ching Chen, Seb Harrevelt, Yu-Chieh Chao, Jyh-Miin Lin, Wei-Hsuan Yu, Hing-Chiu Chang, Chin-Kuo Chang, and Feng-Nan Hwang. 2021. "Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver" Tomography 7, no. 4: 555-572. https://doi.org/10.3390/tomography7040048

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